Home heating is a major factor in worldwide energy use. We describe two
experiments aimed at reducing the amount of time heating systems need to be on,
without compromising occupants' comfort. The first resulted in a machine learning
algorithm based on GPS data to predict when an occupant will arrive at home. The
second examined how long it takes to heat homes based on temperature
measurements, telling us how far in advance arrival predictions are needed. Our
findings suggest that GPS-based prediction has the potential to reduce home
energy consumption compared to existing methods.